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Nonparametric estimation of effect heterogeneityin rare events meta-analysis: bivariate, discrete mixture model

Nonparametric estimation of effect heterogeneityin rare events meta-analysis: bivariate, discrete mixture model
Nonparametric estimation of effect heterogeneityin rare events meta-analysis: bivariate, discrete mixture model
Meta-analysis provides an integrated analysis and summary of the effects observed in k independent studies. The conventional analysis proceeds by first calculating a study-specific effect estimate, and then provides further analysis on the basis of the available k independent effect estimates associated with their uncertainty measures. Here we consider a setting where counts of events are available from k independent studies for a treatment and a control group. We suggest to model this situation with a study-specific Poisson regression model, and allow the study-specific parameters of the Poisson model to arise from a nonparametric mixture model. This approach then allows the estimation of the heterogeneity variance of the effect measure of interest in a nonparametric manner. A case study is used to illustrate the methodology throughout the paper.
1995-0802
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Martin, Susan
57c869c0-9a02-473b-ad80-f20d5e6dd363
Sangnawakij, Patarawan
e821a2a7-a89f-4172-9006-8a6c2db9add6
Jansen, Katrin
82129bd2-7903-41de-b9fe-fe341a176d72
Böhning, Walailuck
c39e39e4-1c54-4688-866a-8541216084f1
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Martin, Susan
57c869c0-9a02-473b-ad80-f20d5e6dd363
Sangnawakij, Patarawan
e821a2a7-a89f-4172-9006-8a6c2db9add6
Jansen, Katrin
82129bd2-7903-41de-b9fe-fe341a176d72
Böhning, Walailuck
c39e39e4-1c54-4688-866a-8541216084f1
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435

Bohning, Dankmar, Martin, Susan, Sangnawakij, Patarawan, Jansen, Katrin, Böhning, Walailuck and Holling, Heinz (2021) Nonparametric estimation of effect heterogeneityin rare events meta-analysis: bivariate, discrete mixture model. Lobachevskii Journal of Mathematics. (doi:10.1134/S1995080221020074).

Record type: Article

Abstract

Meta-analysis provides an integrated analysis and summary of the effects observed in k independent studies. The conventional analysis proceeds by first calculating a study-specific effect estimate, and then provides further analysis on the basis of the available k independent effect estimates associated with their uncertainty measures. Here we consider a setting where counts of events are available from k independent studies for a treatment and a control group. We suggest to model this situation with a study-specific Poisson regression model, and allow the study-specific parameters of the Poisson model to arise from a nonparametric mixture model. This approach then allows the estimation of the heterogeneity variance of the effect measure of interest in a nonparametric manner. A case study is used to illustrate the methodology throughout the paper.

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Accepted/In Press date: 1 August 2020
Published date: 20 April 2021

Identifiers

Local EPrints ID: 448798
URI: http://eprints.soton.ac.uk/id/eprint/448798
ISSN: 1995-0802
PURE UUID: a8fd5e0f-4a3d-4afe-9124-997e1857fa90
ORCID for Dankmar Bohning: ORCID iD orcid.org/0000-0003-0638-7106
ORCID for Susan Martin: ORCID iD orcid.org/0000-0001-8746-0947

Catalogue record

Date deposited: 06 May 2021 16:30
Last modified: 07 May 2021 01:44

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Contributors

Author: Dankmar Bohning ORCID iD
Author: Susan Martin ORCID iD
Author: Patarawan Sangnawakij
Author: Katrin Jansen
Author: Walailuck Böhning
Author: Heinz Holling

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